climate.partial_correlation¶
Provides classes for generating and analyzing complex climate networks.
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class
pyunicorn.climate.partial_correlation.
PartialCorrelationClimateNetwork
(data, threshold=None, link_density=None, non_local=False, node_weight_type='surface', winter_only=True, silence_level=0)[source]¶ Bases:
pyunicorn.climate.tsonis.TsonisClimateNetwork
Encapsulates a partial correlation climate network.
Constructs a static climate network based on partial correlation, as in [Ueoka2008].
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__init__
(data, threshold=None, link_density=None, non_local=False, node_weight_type='surface', winter_only=True, silence_level=0)[source]¶ Initialize an instance of PartialCorrelationClimateNetwork.
Note
Either threshold OR link_density have to be given!
- Possible choices for
node_weight_type
: - None (constant unit weights)
- “surface” (cos lat)
- “irrigation” (cos**2 lat)
Parameters: - data (
ClimateData
) – The climate data used for network construction. - threshold (float) – The threshold of similarity measure, above which two nodes are linked in the network.
- link_density (float) – The networks’s desired link density.
- non_local (bool) – Determines, whether links between spatially close nodes should be suppressed.
- node_weight_type (str) – The type of geographical node weight to be used.
- winter_only (bool) – Determines, whether only data points from the winter months (December, January and February) should be used for analysis. Possibly, this further suppresses the annual cycle in the time series.
- silence_level (int) – The inverse level of verbosity of the object.
- Possible choices for
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_calculate_correlation
(anomaly)[source]¶ Return the partial correlation matrix at zero lag.
Parameters: anomaly (2D Numpy array (time, index)) – the anomaly time series from to calculate the partial correlation matrix at zero lag. Return type: 2D Numpy array (index, index) Returns: the partial correlation matrix at zero lag.
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